Digital system comprising flexible configurable individual policies segmented by lifecycle state, in particular finite state machine engine using share admission control triggered by unplanned amount coverage increase, and method thereof

Abstract

Digital finite state systems and platforms and corresponding methods for processing digital objects by object state trigger processing are presented. The object can be captured by a digital set of defined parameter values, the digital set of defined parameter values comprising at least structural parameter values or state parameter values associated with the object, where the digital set of defined parameter values defines an initial state and / or setting of the object, wherein the object is automatically processed by a digital system by processing the digital object by a series of processing steps by which the digital object transitions from an initial state to a final state. The digital system comprises a finite state machine engine wherein the processing of each digital object is based on a finite state automaton included in or associated with the digital object, the finite state automaton comprising all possible states of the digital object and associated translation rules, the translation rule includes at least a processing step for processing the digital object from one finite state to the next by the finite state machine engine and a condition parameter value. As part of an initial digital object configuration process, the possible state of the digital object and its conversion rules can be configured and implicitly processed within the digital object, where the initial state and / or settings of the object include at least the possible state and the associated conversion rules as the digital object configuration.

Description

Technical Field

[0001] The invention relates to a digital system for processing an object by triggering a process by the state of the object, the object being able to be captured by a set of parameters as structural parameters and / or state parameters and / or other measured parameters associated with the object, which set of parameters defines the initial state and settings of the object. In particular, the object and the set of parameters may, for example, refer to a policy. In particular, the invention relates to an automatic processing system for processing or manufacturing an object or workpiece by a series of technical processes or industrial processes or other processes, by which the object or workpiece passes from an initial state to a final state, such as completion, and wherein the processing is based on finite state machines and finite state automata. Background Art

[0002] Traditionally, in order to process an object or workpiece through a series of industrial, technical or other processes, through which the object or workpiece transitions from an initial to a final state (e.g., completed), a so-called workflow is used, which is a system for controlling, signaling and operating repetitive processes and tasks that occur in a specific order. Workflow processing is widely used for process automation in industry and technology. A workflow represents a technical mechanism and a sequence of operations or processing steps through which machines or processes complete their work on an object or workpiece (whether it is manufacturing a product, processing information or any other technical activity). The object in the workflow can be any kind of object or product to be manufactured or processed, for example, it can also be a digital object, as a digital image in a pattern recognition process or image enhancement process, or a defined set of selected parameters, such as measurement parameters and structural parameters of a physical real-world object or a digital replica of a real-world object or its state. In a special case, such a set of parameter values ​​is called a policy, which captures in particular the physical parameters of the object and its physical interaction with the environment, for example, given by its object-specific exposure measurements to physical real-world events that occur and physically affect the object.

[0003] A workflow system is characterized in that the operations or processing steps are induced, signaled and / or triggered from the outside, i.e., are choreographed. Thus, an object or workpiece is processed by a series of processing steps. Technically, a workflow consists of choreographed and repeatable processing steps or activity patterns, which are implemented by systematically applying resources to the process of transforming objects, workpieces or materials or processing digital data. It can be depicted as a sequence of operations or processing steps of a processing system including one or more simple or complex mechanisms and processing steps. This sequence technically provides the basis for automation. In this context, it must be noted that the term "processing" as used in the technical field is a more general concept than workflow and can be applied to, for example, physical or biological processing, while a workflow is usually a collection of technical processing or processing steps described in the context of processing objects or workpieces, such as all processing that occurs in a machine or automation system environment. An important technical concept in the technical field of workflow systems is the so-called "flow control". Flow control is a technical control concept applied to control and signal workflows to distinguish from static control of buffers or control signals and control commands for materials to represent a more dynamic control of flow rates and flows in motion and in sequences of processing steps. Such an orientation to dynamic aspects is the fundamental basis for preparing for more advanced process control, such as instantaneous or sequential signals of operating units that process objects or artifacts from one workflow state to the next. In this context, it is noted that automated risk transfer claims processing is also an example of a data-intensive, parameter-driven digital workflow based on a set of parameter values ​​defined by the policy, which processing, in particular, sets parameter values ​​for triggering loss coverage due to physical impacts on exposed objects and for the transfer of resources for receiving loss coverage, which are typically related to predicted exposure (i.e. risk) measurements for a defined future time window.

[0004] However, this is a technical problem, which is characteristic of all prior art systems, that the operations or processing steps technically need to be induced, signaled and / or triggered, i.e. orchestrated, from the outside, i.e. by external signals and / or control systems. This does not allow for individual, in particular individually adapted, processing of the workpieces or objects, wherein the adaptation may be related, for example, to the properties of the object, to the properties of its interaction with the environment (e.g. its exposure to external influences), or to geographically dependent properties, etc.

[0005] Another technical problem is related to the automation and digital data processing of loss impact measurement and prediction or simulation. In the traditional life risk transfer industry and technology, finite state Markov chains are usually used as technical structures to capture and parameterize, i.e., to represent different states that are ensured during data processing. Associating monetary amount values ​​with the sojourn time in the state and the transitions between states allows the generation and prediction of the present value of the expected life risk transfer losses within the associated error range at the set interest rate value. Traditionally, these model structures have been used with deterministic interest rate parameter settings and deterministic conversion rate parameter settings, however, in recent life risk transfer predictive modeling and simulation, stochastic modeling structures of interest parameters and conversion rate parameters have gained attention in automation and data processing. This has particular technical significance if the risks associated with changes in the underlying interest parameterization and conversion rate parameters (understood in this article as technical values ​​of the frequency and cumulative event intensity (actual event losses) predicted within a future measurement time window) are to be captured and predicted, and / or if it is desired to hedge such predicted risks by establishing a balancing security based on these same underlying rate parameter settings. A technical life risk transfer setting with a stochastic transition rate setting is that the underlying rate parameters are processed and captured by a finite state Markov chain model structure, and in particular, correlations between rate parameters are implemented within the structure. In prior art systems, there are various technical data processing processes applied to life risk transfer modeling and stochastic interest rates for predicting stochastic mortality parameter values. A combined model structure for stochastic interest rate and mortality rate parameter settings is also known. What several prior art systems have in common is that the interest rate parameters and mortality rate parameters are technically propagated and thus modeled by an engineering model structure representing an affine model process for data processing. This type of processing results in technically tractable modeling and prediction, where the core of the data processing is simplified to solving a system of ordinary differential equations rather than partial differential equations in order to find the expected present value, which has great technical advantages over data processing based on finite state machines of finite state processors. The present invention technically allows the application of affine processing in a more general life risk transfer modeling structure, thereby making it easier and technically more efficient to predict the expected present value through data propagation and simulation.

[0006] It is known that in the prior art system, the finite state Markov chain structure can be applied to the data propagation in the appropriate pricing parameters of modeling risk transfer and specific risk transfer, but it can also be applied to the prediction of credit risk. Credit risk is defined in this article as the actual and / or expected value fluctuations in debt instruments and derivative structures caused by changes in the measurement of the underlying credit quality of the associated counterparty or counter unit. Although the vector autoregression (VaR) model structure applied to daily market risk calculation can generate about 250 predictions in a year, due to its tail structure, the credit risk model structure is technically limited to about one prediction per year. Therefore, it will take a long time to generate enough observation measurement results for the reasonable testing of these data processing structures and the weighting of prediction accuracy. In addition, due to the nature of the credit risk measurement results, only a limited number of historical data on credit losses can be evaluated, which is not enough to process across multiple macroeconomic or credit cycles. These data limitations have caused serious technical difficulties for the verification and calibration of credit risk model structures. It should be mentioned that the vector autoregression model structure is a statistically based structure for capturing and propagating the relationship between multiple quantities and measurements when changing over time. Therefore, all VAR structures are types of stochastic processing modeling. VAR modeling technically generalizes single variable (univariate) autoregressive modeling by allowing for the capture of multivariate time series. Similar to an autoregressive model structure, each measured or captured variable has a relationship (function or equation) that models and propagates its evolution over time. This relationship includes the lagged (past) values ​​of the variable, the lagged values ​​of the other variables in the model structure, and the error term. VAR model structures have the advantage that they do not require as much information about the forces that affect the variables as structural model structures based on simultaneous equations. Technically, the only information required is appropriate filtering of variables that can be assumed to affect each other over time.

[0007] Therefore, the main technical obstacle for model validation (so-called "backtesting"), not only for life risk transfer modeling, but also for credit risk modeling, is the small number of predictions that can be used to calibrate the accuracy of the model's predictive processing. There is a need to provide more flexible modeling and simulation structures that can be used to predict relevant parameter values ​​and propagate them reliably to future time horizons. In addition, the basic credit risk model structure can usually be captured by a two-state Markov chain, where a jump from the initial state represents a default. An extension of this model known in the prior art is to let the default transition rate be modeled as a stochastic process itself, so that it can depend on interest rates and other economic factors. This approach can be applied to various Markov chain model structures. In a more general treatment of the Markov chain method for credit risk modeling with stochastic transition rates, it can be shown how prices generally satisfy a system of partial differential equations. In both approaches, it can be shown how a method can benefit from an affine stochastic process as a factor of transition intensity and economic measurement. If the modeling structure is particularly simple, the Riccati relationship can be used to simplify the modeling problem of solving a partial differential equation system into a modeling problem of solving an ordinary differential equation system, which can have significant technical advantages in terms of data processing efficiency and usage time. The technical method of the present invention has the following advantages in particular: it allows the generalization of these prior art methods that allow risk transfer pricing in a more general decreasing Markov chain model structure. Therefore, although the Markov chain modeling structure is widely used in risk transfer technology and industry, its combination with the configuration elements according to the present invention allows a certain degree of flexibility and dynamics. The system of the present invention can be particularly applied to dual stochastic Markov chain structures, especially in life risk transfer and credit risk prediction and simulation. It can also be applied to the generation of transition probabilities in certain dual stochastic Markov chains, where the resulting output signal can be used, for example, for the valuation of life insurance contracts. Summary of the invention

[0008] The object of the present invention is to provide a more efficient and more accurate and more flexible system and method which allows new, dynamic and flexible adaptable processing and propagation of loss coverage related parameter sets, such as life risk parameters with associated automatic dynamic risk transfer pricing and dynamic premium generation and valuation of policies capturing individual risk transfer settings and parameter values. Another object of the present invention is to allow the use of a finite state machine engine as a technical structure for the processing of flexible policies, replacing traditional static and externally orchestrated workflow systems for automatic processing of objects.

[0009] According to the invention, these objects are achieved in particular by the features of the independent claims. Further advantageous embodiments are also apparent from the dependent claims and the description.

[0010] According to the present invention, the above-mentioned purpose is particularly achieved by a digital finite state system for processing digital objects by triggering processing by the object state, the object can be captured by a digital set of defined parameter values ​​including at least a structural parameter value or a state parameter value associated with the object, wherein the digital set of defined parameter values ​​defines the initial state and / or setting of the object, wherein the digital object is automatically processed by the digital system by processing the digital object by a series of processing steps, and the digital object transitions from the initial state to the final state through the series of processing steps, wherein the digital system includes a finite state machine engine, wherein the processing of each digital object is based on a finite state automaton included in or associated with the digital object, the finite state automaton includes all possible states of the digital object and associated transition rules, the transition rules include at least processing steps and conditional parameter values ​​for processing the digital object from one finite state to the next finite state by the finite state machine engine, and wherein as part of the initial digital object configuration processing, the possible states of the digital object and its transition rules can be configured within the digital object and are implicitly processed, wherein the initial state and / or setting of the object includes at least possible states and associated transition rules as the digital object configuration. In the event of a defined physical impact event, the digital set of defined parameter values ​​may be given, for example, by digital policy parameter values ​​for a policy for loss coverage related to the exposure of the risk exposure unit, the set of digital policy parameters of the policy representing a parameter set of a digital object, wherein the processing state is given by the policy lifecycle state. The conversion rules may, for example, include at least an automatic conversion trigger and / or a conversion threshold and a conversion process including at least a connection process and / or a forging process and / or a joining process. In addition, the automatic conversion trigger may, for example, trigger a conversion automatically or by default, wherein the automatic conversion is triggered when the digital system detects that at least one trigger value or threshold is to be reached or exceeded. The at least one trigger value or threshold may, for example, be continuously or periodically monitored by a finite state machine engine, wherein the trigger triggers a state transition if the parameter value associated with the digital object is reached or exceeded.

[0011] The digital system may, for example, include a computation engine including data structures for capturing and / or storing deterministic conversion rate parameter values ​​and interest rate parameter values ​​for discrete processing of states for data processing according to a policy, wherein portions of the processing are used together with stochastic conversion rates and interest rates for discrete processing for simulation at a portfolio level, wherein the digital system includes an interface to a finite state machine engine, wherein for digital object processing, at least the interest parameter value and / or the conversion rate parameter value are user specific and flexibly configurable and / or selectable from an associated digital library, wherein within the finite state machine engine, the setting parameters and stochastic conversion rates are processed and modeled or simulated by the finite state machine engine, wherein the digital system includes a share generator, wherein the data processing is partitioned by occurring at the share level and accumulated by the premium state and quote level, wherein a share is created each time a policy premium is affected by an unplanned increase in the amount of insurance, and wherein for each newly applied risk transfer, each premium starts with a share, each share being associated with a specific risk transfer version, coverage, and rate at the corresponding point in time when the share is generated. Each finite state of the policy consists of one or more premium shares. The calculation engine processes parameter values ​​on a per-share basis and / or cyclically, thereby providing traceable pricing parameter value generation and output. The calculation engine may, for example, include an object or product framework that sets a full set of permissible configuration options on a product and / or premium and / or rate level, a subset of which may be used to configure individual risk transfer. The product or object framework may also, for example, include a product entity framework, wherein the initial settings of the product framework include at least annual and monthly premium frequencies. The initial settings of the product framework may, for example, include at least annual and monthly premium frequencies, wherein parameter values ​​may be increased over time. The product framework may, for example, also include a premium entity framework that defines a full set of permissible configuration options on a premium level, a subset of which may be used by premiums. The product framework may, for example, also include a rate entity framework that defines a full set of permissible configuration options on a rate level, a subset of which may be used by rates. The applicable premium rate table may, for example, be located at the rate level, so as to have the ability to always apply the latest premium rate table according to the policy share.

[0012] The technical method of the present invention has the following advantages in particular: it allows all possible policy lifecycle states of the entire policy to be processed and managed based on a finite state engine. With this technical method, the possible states of the risk transfer product and its transition rules can be configured in the object or product itself, where the state transition is not performed from an external instance (such as a workflow engine), but it is done implicitly as part of the product configuration. Traditionally, the management of the policy lifecycle in digital systems is done based on predefined workflows. The core concept behind this setup is the idea that a policy is a structure of data points and parameters without any information or implicit logic about its own "behavior", but technically requires external orchestration and modification by a workflow management system. In prior art systems, a policy workflow is defined as a sequence of connected steps, where each step follows a rule or pattern. The workflow management system handles the routing of data (policies) between these steps, handles dependencies, and ensures that tasks are completed in the correct order. In contrast to prior art systems, the system of the present invention allows the use of an implicit object / product configuration approach, and the orchestration of the policy lifecycle is implicitly set by the transition rules in the object / product, which are then considered by the finite state engine to change the policy state. This technical structure captures the object being processed, such as a risk transfer product, through a finite number of states, where a policy can only be in one state at a time. The policy transitions from one state to another in response to some external inputs such as lifecycle events (e.g., customer purchase, policy claim, policy end date reached) and a set of configured transition rules. This makes the behavior of the policy implicit and allows significant changes in the product lifecycle through simple configuration changes in the underlying transition rules. Figure 1 Exemplary state transitions for an insurance policy with associated events are shown. Figure 2 An exemplary implementation of an exemplary, creative finite state transition engine with actual transition examples is shown. This flexible adaptation of state transitions also has the advantage of allowing the behavior of insurance policies to be changed for the requirements of different jurisdictions. Those often have different compliance requirements, such as the preconditions for a policy to take on risk (e.g. customer screening has been completed and identity verification may be required for pre-sale in Germany, but not in the UK). This behavior can be simply configured as a transition rule without having to adjust externally set workflows. This makes it easy to use the platform in a true multi-jurisdictional environment. The same applies to setting different processing requirements for different tenants / operators using the digital system or platform. For example, one tenant may require certain data checks before a policy can take on risk, while another may not.

[0013] In addition, the present invention has the advantage of allowing maximum flexibility when user needs may change, and each instance of a policy consists of multiple premium shares. The data processing system can perform calculations on a per-share basis, ensuring accurate and auditable automatic pricing parameter generation and output. A further advantage is that all calculation-related attributes can be located at the rate level to enhance flexibility, thereby allowing different values ​​according to the policy share. For example, the premium rate table can be located at the rate level, for example, so as to have the ability to always apply the latest premium rate table according to the policy share. Another advantage is that by differentiating the calculation request into micro-calculation requests at the quote, premium and share level, the data processing and / or calculation request can be enhanced by configuration flexibility, and these micro-calculation requests are pre-processed and run in a dynamic order, so that the MTA can be specifically defined by pre-processing rules (and they all use the same core processing rules): (i) pre-processing, i.e., creating micro-calculation requests at the quote, premium and share level and filling them, (ii) core processing, i.e., executing micro-calculation requests by using core calculations, which always remain the same. Another advantage of the present invention is that it provides a technical framework within which it allows life and other accidents to be placed within the same technical framework based on a general, unified, finite-state treatment of concepts such as actuarial values, expected and retroactive premium reserves. Although, the system of the present invention is not completely general in the sense that all (known) forms of life accident insurance appear as special cases. In general, the applicability of the system can be restricted to situations that can be described by finite-state time-continuous machines, for example. However, the system is also able to generalize the technical approach to situations with countably infinite states. Thus, for example, situations with certain forms of selective conversion power can also be covered by the system, although these may require different state spaces. In addition, the system is also able to apply to situations where the insurance benefits depend on the duration in the state, because there is a qualification period for the risk transfer triggered by disability. In this sense, all known prior art systems are not as applicable as the present invention to capture such widely divergent situations.

[0014] It is to be noted that the technical setup for Markov chain-based generation of risk transfers and cash flows in the platform can be implemented flexibly, as also described in the patent application, but such generation and evaluation itself is generally stateless. What is considered part of the modeling approach are certain states of the insurance premium and their transformation in a probabilistic context in the Markov chain-based evaluation and generation, but this applies to the risk itself and not to the policy lifecycle states (see Figure 3 ). BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The invention will now be described in more detail by means of examples and with reference to the accompanying drawings, in which:

[0016] Figure 1 A block diagram schematically illustrating exemplary state transitions of an insurance policy as a digital object to be processed with associated events is shown.

[0017] Figure 2 A block diagram schematically illustrating an exemplary implementation of a state transition engine using actual transition examples is shown.

[0018] Figure 3 A block diagram schematically illustrating an exemplary state transition cash flow modeling structure and / or a 3-state transition cash flow modeling structure, ie, an exemplary premium / share state for the Markov chain structure of the present invention is shown.

[0019] Figure 4 A block diagram schematically illustrating an exemplary relationship between configuration and computation, as well as a hierarchy of the configurable elements themselves, is shown.

[0020] Figures 5 to 7 A block diagram schematically shows an exemplary overview of a share, wherein Figure 5 shows the color legend, and Figure 6 The corresponding share processing is shown.

[0021] Figures 8 to 9 A block diagram schematically illustrating an exemplary overview of how a Markov chain modeling structure is used is shown, wherein Figure 8 shows a simple Markov chain modeling example, and Fig. 9 A simple Markov chain calculation of the premium cash flow linked to state A is shown. Fig. 9 An example of 1000 customers is shown: in the first year, 100*1000=100,000 are received. If 20% of the customers die in the second year, the active customers are 800, and 90*800=72,000 are received (instead of 90*1000=90,000).

[0022] Fig.10 A block diagram schematically illustrating an exemplary logical architecture of the digital system / platform and method of the present invention is shown.

[0023] Fig.11A block diagram schematically illustrates the services that can be provided by the digital platform in addition to the data processing and prediction of the present invention: (i) storage of all risk transfer and pricing parameters, (ii) calculation engine that automatically generates quotes, premium or refund parameter values ​​for customers, (iii) flexible configuration of cash flows attached to states and state transitions, which is based on stochastic processing. However, it is not clear how to implement it in practice for the purpose of risk transfer and embed it into the proposed wider solution, (iv) configuration-based calculations - features that allow key calculations such as "sum-date-year" to be configured rather than hard-coded, (v) premium shares - in order to allow maximum flexibility as user needs may change, each instance of a policy consists of multiple premium shares, and the calculation service can perform calculations on a per-share basis, ensuring accurate and auditable automatic pricing parameter generation and output.

[0024] Fig.12 A block diagram schematically illustrating an exemplary technical integration structure and pattern for associating third-party systems is shown.

[0025] Fig.13A block diagram schematically illustrating an exemplary calculation framework and a product framework is shown. The calculation framework includes (A) the following calculation components: (1) a calculation request refers to a data set for a specific process, product, and policy (unless the calculation request is about creating a policy), (2) a calculation rule is a set of rules for calculating attribute values ​​and attribute properties, and (3) a calculation response is a data set representing a specific policy; and (B) the following calculation entities: (1) a calculation entity is a set of attributes and their attribute properties, which are subject to certain calculation rules. The figure on the right shows entity relationships related to calculations from different domains. In addition, the product framework defines the entire set of configuration options that can be allowed at the product / premium / rate level, a subset of which can be used by products: (A) The product entity framework defines the entire set of configuration options that can be allowed at the product level, a subset of which can be used by products. Example: The initial product framework may allow annual and monthly premium frequencies, and may increase over time to allow semi-annual and quarterly premium frequencies. (B) The Premium Entity Framework defines the full set of allowable configuration options at the Premium level, a subset of which can be used by a Premium. Example: an initial Premium Framework might allow an override type of "Level", and might be enhanced over time to also allow "Reduction", and (C) The Rate Entity Framework defines the full set of allowable configuration options at the Rate level, a subset of which can be used by a Rate. Example: an initial Rate Framework might allow the frequency of reductions to the Premium amount to be reduced annually, and might be enhanced over time to also allow monthly. In general, calculation-related properties will be located at the Rate level to enhance flexibility and allow different values ​​based on the policy share. Example: a Premium Rate Schedule might, for example, be located at the Rate level to have the ability to always apply the latest Premium Rate Schedule based on the policy share.

[0026] Figures 14a to 14cA block diagram schematically illustrating an overview of an exemplary technical design is shown. (A) In order to achieve extensibility (easy to change and extend) and decoupling of the configuration of a computation from its execution: (i) All computation elements are or can be expressed as functions that receive the current quote period as an argument and return a decimal number. See the separate pdf for a full list of functions. (ii) The returned "BigDecimal" allows us to represent any kind of numbers: double, floating point, integer, ..., so internally, all operations will be done with "BigDecimal"... and all defined as configuration properties will be parsed as computation elements. (iii) Example "special" functions: present value; insurance amount configuration; (B) The output is a collection of trees of computation elements: (i) Any output element can be expressed as a combination of some basic computation element and its subordinate computation elements. A computation element can be seen as the root of a tree of all its dependencies that need to return a decimal number. The leaf nodes of this tree will always be inputs or parameters or any other independent computation elements, (ii) configuration properties and their relationships to computation elements, (iii) all configuration properties will define functions<CoverPeriod,BigDecimal> , (iv) Input: The CalculatorInput interface defines the input to our calculator. We have chosen a type-safe implementation of heterogeneous containers. (v) Output: The CalculatorOutput interface defines the output of our calculator - in order to be flexible enough for any kind of quote model, the output is simply a collection <bigdecimal>The associated name dictionary. (vi) Validators are also functions: ComputeElementValidator: The ComputeElementValidator interface is also a function, but in this case a predicate.<CoverPeriod,BigDecimal> Two implementations are provided, but are not parsed from configuration properties to simplify PoC implementations (PoC implementations can be easily added to compute factories since compute provides PoC in its API).

[0027] Fig.15 A block diagram schematically illustrating an exemplary calculation process is shown: (i) After all calculation elements are created according to their configuration. All attribute correspondences are obtained and the created calculation is passed to the calculator. (ii) Quote calculation is included in the above figure. (iii) Share management / building quotes is also included in Fig.13 In addition, the digital system may include, for example, (a) stream parallelization in the calculator, (b) more implementations of the calculation elements + calculation elements that have been built in the calculation factory, (c) coverage period years..., without having to calculate all elements again, (d) enhanced calculation request configuration flexibility by differentiating calculation requests into micro-calculation requests at the quote, premium and share level, which are pre-processed and run in a dynamic order, so that the MTA can be specifically defined by pre-processing rules (and all of them use the same core processing rules): (i) pre-processing, i.e. creating micro-calculation requests at the quote, premium and share level and filling them, (ii) core processing, i.e. executing micro-calculation requests by using core calculations, which always remain the same.

[0028] Fig.16 A block diagram schematically illustrating an exemplary Technical Settlement (TA) structure is shown through a process flow diagram of the solution. The goal of the Technical Settlement domain is to provide source settlement data to support the ledger system (LFD). The solution allows event-based policy management systems to integrate with traditional RDBMS LFS platforms. In addition, it allows the provision of output policy level settlement data via both DLE and control reports, allowing data to be cross-checked directly with the source system.

[0029] Fig.17 A block diagram schematically illustrating an exemplary technical settlement (TA) structure is shown. This implementation variant uses (i) DDD (domain driven design), (ii) CQRS (command query responsibility separation), and (iii) ES (event source). It accumulates with ES: (a) settlement plan (b) remittance, (c) arrears. It accumulates without ES: (a) quotation. ES+CQRS is implemented using a lightweight framework. ES events are internal and are not events in Kafka topics, but events in the internal event store. It only belongs to the technical settlement service. The system uses a hexagonal architecture, and projects can be divided into modules according to concerns. The domain only knows the interface of the persistent storage, but does not know the underlying storage / implementation.

[0030] Fig.18 A block diagram schematically illustrating an exemplary policy structure (reconstructed) is shown. The policy structure and services hold data related to the policy and drive the policy lifecycle, listening to all other services and understanding the policy / customer at any given point in time. This includes third-party services (i.e., customer / company data and their role in individual policies). Implementation variants include, for example, (i) definable rules, behaviors, and interactions of "product / calculation" and "policy" services, as well as the operations and validations required to make the policy effective, plus the associated premiums and riders - all of which are configuration driven, (ii) premium shares - in order to allow maximum flexibility as customer needs change, each instance of a policy consists of multiple premium shares. While shares exist in other systems, it is not known in the prior art that the present invention uses shares and interacts with shares across systems to generate policy changes (mid-term adjustments).

[0031] Fig.19 A block diagram schematically illustrating an exemplary policy structure (refactored) is shown. Design principles for the refactored policy structure and service design may include, for example, (i) isolation of application and domain logic, (ii) domain-driven design, (iii) improved REST API for policy management to follow business processes, and (iv) development of event patterns to simplify messaging.

[0032] Fig. 20 A block diagram schematically illustrates an example of asynchronous share admission, i.e. updating the state when an unplanned request for an increase in amount coverage occurs and / or when a loss is detected by the system. Fig.17 Reference numerals in : (1) reject the request to create a share, (2) accept the request to create a share of type i, and (3) accept the request to release a share of type j. The gray area above is the admissible area.

[0033] Fig.21 A block diagram of an electronic digital finite state system 1 is shown, exemplarily illustrating, for automatically processing a digital object 132 by object state triggered processing 131, the digital object 132 being captureable or defined by a parameter set 1321 having definable parameter values, the parameter set 1321 comprising at least structural parameters 13211 or state parameters 13212 associated with the digital object 132, wherein the digital set 1321 of defined parameter values ​​defines an initial state and / or setting 1322 of the object 132, wherein the digital system 1 automatically processes the digital object 132 by processing the digital object (132) by a series of processing steps 1311, the digital object 132 transitioning from an initial state 13221 via an intermediate state 13222 to a final state 13223 by the series of processing steps 1311.

[0034] Fig. 22 A block diagram schematically illustrating the triggering of automatic digital twin structure applicability controlled by the output signal 143 generated by the digital individual measurement engine 3 based on the measured parameter values ​​of the wearable device / IoT sensor 15, and on the other hand, the feedback to the simulation engine 12 and / or the Markov chain structure 11 and / or the finite state machine engine 13 monitored and / or detected by thresholding the digital twin parameters is shown. DETAILED DESCRIPTION

[0035] Figures 1 to 22 Schematically shown is an architecture of a possible implementation method for an embodiment of an electronic digital finite state system 1 of the present invention and a corresponding method for automatically processing a digital object 132 by object state triggered processing 131, wherein the digital object 132 is capable of being captured or defined by a parameter set 1321 with definable parameter values, the parameter set 1321 comprising at least structural parameters 13211 or state parameters 13212 associated with the digital object 132, wherein the digital set 1321 of defined parameter values ​​defines an initial state and / or setting 1322 of the object 132, wherein the digital system 1 automatically processes the digital object 132 by processing the digital object (132) by a series of processing steps 1311, and the digital object 132 transitions from an initial state 13221 via an intermediate state 13222 to a final state 13223 through the series of processing steps 1311.

[0036] The digital system 1 includes a finite state machine engine 13, wherein the processing of each digital object 131 is based on a finite state automaton 1323 included in or associated with the digital object 131. The finite state automaton 1323 includes all possible states 13231 of the digital object 132 and associated conversion rules 13232. The conversion rules 13232 include at least a processing step 132321 and a conditional parameter value 132322 for processing the digital object 132 from one finite state 1322 to the next finite state 1322 by the finite state machine engine 13. As used herein, a finite state automaton or state machine 1323 is a machine structure that can be in exactly one state from a finite state set at a given time. An important subset of states includes initial states. When the automaton is turned on, it can be in any initial state. When inputs from finite input variables are fed to the automaton and certain conditions or constraints are met, the automaton can leave its current state and switch to the next state. This is called a state transition. If the automaton is in a final state when there are no more inputs, it is said to have accepted its input.

[0037] In the system of the present invention, as part of the initial object configuration process 133, possible states 13231 of a digital object 132 and its transformation rules and / or functions 13232 can be configured within the digital object 132 and processed implicitly, wherein the initial state and / or setting 13221 of the object 132 comprises at least the possible states 13231 and the associated transformation rules 13232 as the initial digital object configuration 13233. In the event of the occurrence of a defined physical impact event 25, the digital set 1321 of defined parameter values ​​may be given, for example, by digital policy parameter values ​​132131 for a policy 13213 for loss coverage related to the exposure of the risk exposure unit 2, wherein the processing state 1322 is given by the policy lifecycle state 132132. The conversion rule 111 may, for example, include at least an automatic conversion trigger 132323 and / or a conversion threshold 132324 and a conversion process 132325, wherein the conversion process 132325 includes at least a connection process 1323251 and / or a forging process 1323252 and / or a joining process 1323253. In addition, the automatic conversion trigger 132323 automatically or by default triggers the conversion, wherein the automatic conversion is triggered when the digital system 1 detects that at least one trigger value or threshold 132324 is to be reached or exceeded. The at least one trigger value or threshold 132324 may, for example, be monitored by a finite state machine engine 13, wherein the trigger 132323 triggers a state conversion if a parameter value associated with the digital object 132 is reached or exceeded. The policy lifecycle state of the entire policy can be processed and automatically managed based on a finite state engine. With this approach, the possible states of a digital object (e.g., an insurance product) and its conversion rules can be configured in the digital object / product itself. State transitions are not performed from an external instance (like a workflow engine), but are done implicitly as part of the object / product configuration.

[0038] The digital system and digital platform of the present invention use an implicit object / product configuration approach, and the orchestration of the policy lifecycle is implicitly set by the transition rules in the digital object / product, which are then considered by the finite state engine to change the policy state. It is a computational structure that parameterizes the risk transfer product using a finite number of states, where a policy can only be in one state at a time. The policy transitions from one state to another in response to some external inputs such as lifecycle events (e.g., customer purchase, policy claim, policy end date arrival) and a set of configured transition rules. This makes the behavior of the policy implicit and allows significant changes to be made in the object / product lifecycle through simple configuration changes in the underlying transition rules. See for example state transitions for a policy with associated events. Figure 1 , and an exemplary implementation of a state transition engine using actual transition examples Figure 2 .

[0039] The digital system may, for example, include a computation engine 11 including data structures for capturing and / or storing deterministic conversion rate parameter values ​​and interest rate parameter values ​​of discrete processes of states for data processing according to a policy, wherein portions of the processing are used together with stochastic conversion rates and interest rates of the discrete processes for simulation at a portfolio level, wherein the digital system includes an interface to a finite state machine engine, wherein for digital object processing, at least the interest parameter value and / or the conversion rate parameter value are user specific and flexibly configurable and / or selectable from an associated digital library, wherein within the finite state machine engine, the setting parameters and stochastic conversion rates are processed and modeled or simulated by the finite state machine engine, wherein the digital system includes a share generator, wherein data processing is partitioned by occurring at a share level and accumulated at a premium state and quote level, wherein a share is created each time a policy premium is affected by an unplanned increase in the amount of insurance, and wherein for each newly applied risk transfer, each premium starts with a share, each share being associated with a specific risk transfer version, coverage and rate at the corresponding point in time when the share is generated. Each finite state of the policy consists of one or more premium shares. The calculation engine processes parameter values ​​on a per-share basis and / or cyclically, thereby providing traceable pricing parameter value generation and output. The calculation engine may, for example, include an object or product framework that sets a full set of permissible configuration options on a product and / or premium and / or rate level, a subset of which may be used to configure individual risk transfer. The product or object framework may also, for example, include a product entity framework, wherein the initial settings of the product framework include at least annual and monthly premium frequencies. The initial settings of the product framework may, for example, include at least annual and monthly premium frequencies, wherein parameter values ​​may be increased over time. The product framework may, for example, also include a premium entity framework that defines a full set of permissible configuration options on a premium level, a subset of which may be used by premiums. The product framework may, for example, also include a rate entity framework that defines a full set of permissible configuration options on a rate level, a subset of which may be used by rates. The applicable premium rate table may, for example, be located at the rate level, so as to have the ability to always apply the latest premium rate table according to the policy share.

[0040] It must be noted that the finite state automaton and engine 13 of the present invention can be applied to many practical technical problems, not just to risk transfer technology. For example, nowadays, when workflow-based programming is gaining more and more importance in programming technology. Workflows can be mapped to automata that manage any kind of rule-based processing that drives workflows. Although complex technical frameworks that support workflow-based programming have emerged, it is important to make the implementation more technically applicable and granular, especially more controllable at the processing step level, in various cases. This should be done without exposing too many parts of their internal behavior or forcing them to set up a complex environment or even using additional design tools. The system of the present invention provides a technical structure that allows those technical problems of the finite state automaton of the prior art to be overcome. The system of the present invention introduces a simple and effective technical way to integrate finite state automata into any rule-oriented application. This has a positive technical effect on the efficiency, manageability and testability of the completed software in particular. When discussing rule-based processing or finite state automata in this application, possible states and state transitions are specified together with state transition conditions, which is how the automaton is described in this article. Any form of description must be technically converted into functional rules. The transformation can happen automatically, so if necessary, the implementation can even be generated dynamically. This is one of the important technical advantages of the present finite state machine engine 13, which has possible states 13231 of the digital object 132 and its transformation rules 13232, which form part of the digital object 132 to be processed itself and can be configured within the digital object 132 as part of the initial object configuration process 133 and are processed implicitly.

[0041] Furthermore, the electronic digital finite state system 1 may, for example, include a Markov chain modeling structure and / or predictor for mortality probability parameter value propagation 121 and for dynamically and automatically influencing coverage pricing 122 by processing a plurality of individual mortality related measured parameters 123 associated with a portfolio 124 of loss coverages 1241 of risk exposed individuals 2, wherein each loss coverage 1241 held in association with the portfolio 124 is set by a risk transfer parameter 12411 of the loss coverage 1241 to define a risk transfer policy for a parameter-based individual risk transfer 1241. The Markov chain modeling structure 11 is applied in combination with configurable elements including at least states 114 and / or state transitions 113 and / or cash flows 112 specific to the risk transfer 1241, and wherein stochastic Markov data processing is applied to the Markov chain structure 11 through a series of possible events 25 / 25i, wherein the probability value of each event 25 / 25i depends only on the state 114 obtained in the previous event 25. The digital system 1 comprises a simulation engine 12, the simulation engine 12 comprising a data structure 11 for capturing and / or storing deterministic conversion rate parameter values ​​113 and interest rate parameter values ​​112 of a state discrete process 121 for data processing according to an insurance policy 1241, wherein portions of the process 12 are used together with the stochastic conversion rates 113 and interest rates 112 of the discrete process for simulation at a portfolio level. The digital system 1 comprises an interface 13 to the Markov chain structure 11, wherein for the stochastic Markov data process 2, the interest parameter values ​​112 and conversion rate parameter values ​​113 are user specific and flexibly configurable and / or user specific selectable from an associated digital library 125, and wherein within the stochastic Markov data processing settings 111, ..., 114 and the stochastic conversion rates 113, these underlying rates 113 are processed and modeled by the finite state Markov chain structure 11. For data processing performed by the finite state Markov chain structure 11, one or more transition functions 111 are configurable via the data interface 13 and / or are selectable from a digital library 125, the transition function 111 linking at least two states 114 within the Markov chain structure 11, wherein all states 114 of the Markov chain structure 11 are linked to previous states and subsequent states 114 that provide data processing for the entire configurable Markov chain structure 11.

[0042] The digital system and platform 1 comprises a signal generator 14 which can, for example, automatically generate electronic signals 140, ..., 145 based on the output parameter values ​​of the electronic Markov chain structure 11, which are transmitted to an automatic underwriting system of the digital system 1, which triggers at least one automatic underwriting process 141 by automatically assigning at least one risk-exposed individual 2 processed by the Markov chain structure 11 to a risk transfer 1241 associated with a future occurrence of a physical event 25 that physically affects the at least one risk-exposed individual 2. The signal generator 14 can, for example, automatically generate electronic signals 140 based on the output parameter values ​​115 of the electronic Markov chain structure 11, which are transmitted to an automatic digital portfolio management system 1421 of the digital system 1, which automatically adjusts a threshold value of at least one risk-exposed individual 2 processed by the Markov chain structure 11, the at least one risk-exposed individual 2 being automatically assigned to a portfolio 124, wherein the loss and / or damage and / or injury of the individual 2 associated with the future occurrence of a physical event 25 that physically affects the at least one risk-exposed individual 2 is automatically covered by the system 1.

[0043] As discussed, the setup for the evaluation and generation of risk transfer and cash flows can be based, for example, on Markov chains in digital systems and digital platforms and can be done flexibly as described below, but such evaluation itself is usually stateless. As part of the simulation approach, in the Markov chain-based evaluation, certain states of the insurance premium and their transformation in a probabilistic environment are considered, but this applies to the risk itself and not to the policy life cycle states (see Figure 3 ). Thus, the life risk parameter propagation of the present invention is performed and dynamic and automatic risk transfer pricing is performed by a digital system by processing a plurality of individual risk-related parameters associated with a portfolio of risk transfers of risk-exposed individuals. Pricing is performed dynamically and also dynamically generates risk transfer premiums for individual policies using a combination of a Markov chain modeling structure and configurable elements including at least states and / or state transitions and / or cash flows specific to the product instantiated therein. Each risk transfer held in association with the portfolio is set by risk transfer parameters of the risk transfer policy defining the individual risk transfer. A combination of a Markov chain modeling structure and configurable elements including at least states and / or state transitions and / or cash flows specific to the risk transfer is applied. Stochastic Markov data processing is applied to the Markov chain structure through a series of possible events, where the probability value of each event depends only on the states obtained in the previous events. As understood herein, a policy is a system of deliberate rules and parameter settings to guide risk transfer and achieve risk coverage as a rational outcome. A policy can, for example, be implemented as a program or protocol or parameter value setting that defines the behavior of risk transfer (i.e., the operation of the automatic risk transfer system during risk coverage). Life risk is understood in this context as a physical measure of the probability that certain events will occur during a person's lifetime. For example, for cancer this can be given as a measurable probability value that a particular person without certain types of cancer will develop or die from certain types of cancer during his or her lifetime. The probabilistic measure of risk can, for example, be evaluated and / or predicted by the system over certain future time windows or time horizons. The system of the present invention remains within the framework of continuous time. Payment transfers triggered on a discrete time basis can be viewed in the system as a series of funds in continuous time and therefore do not require special consideration. The force of interest δ will be considered to be constant at all times. The generalization to the case of time-dependent or even random δ is trivial. As usual, v=e -δ .

[0044] The technical structure is constructed based on a time-continuous Markov chain model with a state space {1, 2, ..., N}, where N ≥ 2 (see below for the extension to countable state spaces). A sample path of the chain will be represented by the development of a specific risk transfer policy. Time 0 can be defined as the time when the risk transfer policy takes effect. The state of the policy at time t is denoted by S(t) in this paper, and these states are numbered so that S(0) = 1. At t ≥ s, the transition probability of the treatment can be defined by

[0045] P jk (s,t)=P{S(t)=k|S(s)=j}.

[0046] For all j, k, s ≥ 0, when t approaches s from above, one can assume, for example, that P jk (s, t) = δ jk , where δ jk is the Kronecker function. Additionally, it can be assumed, for example, that the transition force from state j to state k is defined by

[0047]

[0048] exists for s ≥ 0 and is a continuous function of s. For s ≥ 0, the resultant force decreasing from state j can be defined by

[0049]

[0050] where μ j (s) = ∑ k≠1 μ jk (s).

[0051] With these assumptions, for all s ≥ 0, each P jk (s,.) is continuous for all t ≥ s. Similarly, for each t > 0, each P ik (., t) is continuous in all closed intervals [0, t]. It can also be shown that in this case, the number of transitions completed by the sample path in any restricted time interval is stochastically less than some Poisson-distributed random variable.

[0052] In the method of the present invention, the simplest kind of risk transfer policy has only two states. The policy is in effect in state 1 and expires immediately after entering state 2. (Naturally, at the end of the risk transfer period, it may also expire in state 1. This treatment can be applied to all types of risk transfer and will not be repeated in each example.) Ordinary life risk transfer and life annuities can be of this type. It is referred to herein as a single portfolio single decrement policy. The second type of policy has N > 2 states, but the policy still expires immediately after decrementing from state 1. (Joint life annuities are an example.) This is referred to herein as a single portfolio multiple decrement policy. The policy may be in effect in several states of the system. If there are m such states, it is called an m-portfolio policy, and these m states are called portfolio states. Such a risk transfer policy with an unspecified value of m is a multiple portfolio policy. Ordinary disability annuities can provide an example.

[0053] Generally, m < N, and there is a set of N - M states with the property that the policy expires after entering the states in this set. Such states can be called external states. However, life annuities with k-year payment transitions definitely show that in some cases, all states of the policy are portfolio states (N = m).

[0054] When a risk transfer policy is maintained in some set of states, the payment transfers are usually taken as annuities, as a fund, or as a lump sum payment upon transition from one state to another. However, some interesting classes of policies differ from this pattern. Certain life annuities with k-year payment transfers mentioned above are case in point. The external state is occupying, and this may also be the case for some portfolio states. However, more often, portfolio states are transient, i.e., once left, the probability of returning to such a state is less than 1. If such a probability of return of a state is equal to zero, it is called a strongly transient state in this paper. Such states are very common in risk transfer structures. As long as the risk transfer policy is in force, its changes are observed, and observations cease after expiration.

[0055] To build a Markov chain modeling structure, you must select the transition forces. For example, if the disability risk transition structure has three states in the Markov chain modeling structure, such as "active" (or state 1), "disabled" (or state 2), and "dead" (or state 3). In notation, etc., where the top letter a stands for "active", and the top letter i stands for "inactive". As indicated by writing the age x in brackets (which represents the entry age of the beneficiary), the probability can, for example, be chosen in the sense that it can depend on all three variables x, s and t, respectively, and not just on x+s and t. Thus, for example, is the probability that a person entering the period at age x who is disabled at age x+s should have recovered by age x+s+t.

[0056] The power of conversion is (i) the power of death in a healthy person: (ii) Disability: μ 12 (t) = v [x]+t , (iii) Restoration force: μ 21 (t) = ρ [x]+t , and (iv) the mortality of disabled persons: When state 3 is occupied, μ 31 (t)≡μ 32 (t)≡0. It should be noted that the age in brackets in these notations is always the age at the time the policy takes effect. Thus, for example, ρ [x]+t It is not the resilience of a person of age x+t to disability at age x.

[0057] To summarize what has been illustrated above, it can be noted that conversion forces can depend on variable and / or parameter values ​​(such as "insurance age"), the value of which remains fixed throughout the observation period, as well as on the duration t since issuance. In structures such as life risk transfer and simple life and death treatments on which life annuities are based, the present structure is therefore sufficient to illustrate ordinary kinds of selected mortality rates. Since the values ​​of the fixed variables are given immediately from the time of policy issuance, the corresponding conversion forces are called issuance selections. Conversion forces in disability risk transfer may depend on variables such as the age of current disability and the duration of current disability, rather than on the insured age and the duration since issuance. In cases where the value of a conversion force at time t can depend on the duration in the state S(t) that the sample path stays, these forces can be called current state selections. Therefore, for this example, a distinction can be made between two kinds of selection forces. Obviously, structures can be envisaged in which the conversion forces are selected in two ways. The three-state disability treatment can again be used as an example. It can be envisaged that the restoring force ρ {x}+{u}+t =(t) = t, which may all depend on the age at entry x, the age at the last disability x + u, and the duration of the current disability t. The theory in this paper does not account for the switching power of the current state selection. For simplicity, the possibility of issuing selection power can be ignored, for example, from the beginning.

[0058] The digital system includes a calculation engine that uses discretely processed deterministic transition rates and interest rates to perform calculations based on insurance policies, wherein portions of the processing are used together with discretely processed stochastic transition rates and interest rates to perform simulations at a portfolio level. In particular, the calculation engine includes a data structure for capturing and / or storing state discretely processed deterministic transition rate parameter values ​​and interest rate parameter values ​​to perform data processing based on insurance policies, wherein portions of the processing are used together with discretely processed stochastic transition rates and interest rates to perform simulations at a portfolio level.

[0059] The digital system comprises a user interface to a Markov chain structure, wherein for a random Markov data processing, the interest parameter values ​​and the conversion rate parameter values ​​are user specific and flexibly configurable and / or selectable from an associated digital library, and wherein within the random Markov data processing settings and the random conversion rates, these underlying rates are processed and modeled by a finite state Markov chain structure. The digital library may, for example, be accessible to multiple users, wherein a user's conversion function and / or interest parameter value and / or conversion rate parameter value may be accessible to another user via a data interface and may be applicable to another user's finite state Markov chain structure. A first user's conversion function and / or interest parameter value and / or conversion rate parameter value may, for example, only be accessible upon request by a second user and / or upon approval or activation by the first user to the second user.

[0060] For data processing performed by a finite state Markov chain structure, one or more transition functions are configurable via a data interface and / or are selectable from a digital library, the transition function linking at least two states within the Markov chain structure, wherein all states of the Markov chain structure are linked to previous states and subsequent states that provide data processing for the entire configurable Markov chain structure.

[0061] As an embodiment variation, the correlation between rate parameters can be applicable, for example, within a finite state Markov chain structure. The finite state Markov chain structure can, for example, also include elements that provide flexible configuration for the use of a combined model structure for random interest parameter values ​​and / or mortality parameter values. The random Markov data processing of the interest parameter and the mortality parameter can be configured by an affine data processing structure, wherein the finite state Markov chain structure becomes a model structure that can be tracked during the propagation of the parameter values ​​to a defined future time window. For flexible configuration, the digital system can, for example, include applicable computational profiles and / or trees that can be processed by a computational engine of the digital system. The Markov chain structure can, for example, be implemented as a continuous time Markov chain structure having a finite or countably infinite state space that provides a random processing for parameter propagation. In addition, the Markov chain structure can, for example, be provided by a digital system only to a time-uniform Markov chain processing, wherein all probability values ​​that provide a measure of life risk within a future time window are generated based on the Markov property.

[0062] The system may, for example, include a share generator wherein data processing is partitioned by occurring at a share level and accumulated by premium status and quote level wherein a share is created each time a policy premium is impacted by an unplanned increase in the amount of insurance, and wherein for each newly applied risk transfer, each premium begins with a share, each share being associated with a specific risk transfer version, coverage, and rate at the corresponding point in time when the share was generated. Thus, calculations may, for example, occur at a share level and accumulated by premium and quote level wherein a share is created each time a policy premium is impacted by an unplanned increase in the amount of insurance, and wherein for new business, each premium begins with a share, each share being associated with a specific product version, coverage, and rate at the corresponding point in time when the share was created.

[0063] The present invention allows for the dynamic generation and evaluation of premiums for individual policies using a combination of established Markov chain models and configurable elements (states, state transitions, cash flows) specific to the product it instantiates. The system includes a computation engine that uses discretely processed deterministic transition rates and interest rates to perform calculations based on the policies. The system may also use, for example, portions of this computation engine where discretely processed stochastic transition rates and interest rates can thereby support simulations at a portfolio level.

[0064] The data processing and / or calculations of the present invention occur at the share level and are accumulated at the premium and quote level (e.g. Figure 1 ). Shares are created whenever a policy premium is affected by an unplanned increase in coverage (insurance), i.e., for new business, each premium starts with one share. Each share is associated with a specific product version (i.e., the risk transfer structure version, coverage and rates associated at the corresponding point in time when the share was created). While Markov chain modeling structures are widely used in automated insurance and risk transfer technology and other industries, their combination with configuration elements on the share level allows a degree of flexibility and dynamism that is novel and unknown in the prior art.

[0065] The technical characteristics of shares in data processing of Markov chain modeling structures require efficient share management. To this end, a mechanism of share admission control can be applied in the system of the present invention, which operates in the form of a state machine. As described above, shares are created whenever the policy premium is affected by an unplanned increase in the amount of coverage (insurance), that is, for new business, each premium starts with one share. As an implementation variant, the share generator includes an electronic share admission control triggered by a general state model for synchronous share admission control, and in other implementation variants is used for asynchronous share admission control. Technically, it can be shown that the share generator is Markov under a set of weak constraints. As a further implementation variant, an approximation of the state transition matrix is ​​also disclosed below to reduce the computational complexity in actual technical applications.

[0066] The partitioning triggered by the unplanned increase in coverage, i.e. the generation and / or application of encapsulated Markov shares in the event of detecting that the policy premium is affected by the unplanned increase in coverage (insurance), is one of the basic features of the system of the invention and one of the most important enabling factors for the technical operability of the system of the invention. It allows the system to automatically manage and utilize its physical and virtual resources in the form of logically independent virtual risk transfer structures, i.e. the technical automatic resource infrastructure for payment transfer and the ability to virtualize conversion functions or conversion processing, also known as the unplanned increase in coverage shares. It provides a wide range of technical improvements in scalability, flexibility, accountability, shareability and profitability for the automatic invention system.

[0067] A technical goal and problem brought about by covering increased shares is to efficiently allocate resources across different shares to achieve better utilization efficiency. More specifically, there are two typical scenarios of such inter-share resource management. First, when the system provides direct loss and / or risk coverage to end users through resource transfers and maintains loss / risk coverage on its own scalable shares. Second, in a resource sharing use case that can be referred to as shares as a service, where, as an implementation variant, the system can package its resources into standardized shares and rent them out to third-party systems, such as virtual increases covered by shares, and where periodic income is provided to such third-party systems by the shares. In both scenarios, the system aims to maximize overall resource utilization (e.g., rate of return) by adjusting its resource allocation subject to the constraints of resource pool limitations and / or subject to final adjustment rules.

[0068] Compared with other Markov chain modeling structure optimizations, the share method is less challenging due to the random nature and fluctuating behavior of the demand for collective resources. Applying this technical framework, the system can, for example, make a binary decision for each share admission, that is, accept or reject a request for a triggered increase in a new share. The system is regarded as a state machine, and the state transition is caused by the response to the incoming share request. Although this structure has advantages, as an implementation variant, the structure of the system can be further optimized. Share requests can essentially come, for example, in an asynchronous manner, resulting in an asynchronous state modeling structure, and some implementation variants can rely on a synchronous framework for resource control and management. Secondly, the structure of the system can, for example, only support simulation of the system's partitioning strategy or development-based triggering. As other implementation variants, the state model structure of the share admission system can operate in a synchronous method, that is, when the system autonomously and periodically makes decisions on requests for unplanned amount coverage increases, rather than making decisions immediately after requesting an unplanned amount coverage increase or loss detection, where the disclosed synchronous state modeling structure is Markov. In summary, it must be noted that the inventive system as proposed herein allows application to asynchronous share admission systems, technically allowing the definition of key concepts such as resource feasibility and partitioning strategies. However, the inventive system can also be based on a synchronous share admission modeling structure, which can be shown to be Markov when the statistics of requests for unplanned amount coverage increases are memoryless. The system allows automatic evaluation of state transition probabilities both in single steps and in the long term. The system structure is completely transparent, which allows numerically determining the feasibility of the applied Markov model structure and the accuracy of the proposed processing of the state transition matrix of the Markov chain.

[0069] In the risk transfer technique and in order to realize an automated system, a resource pool having M different countable resources can be represented by a vector r = [r1, r2, ..., r M ] T Consider N different share types, where for each share type n∈[1,2,…,N], its cost resource bundle c n =[c 1，n , c 2，n , ..., c M，n ] T To maintain shares. All shares are units. Therefore, the system manages its resources autonomously by adjusting the set of active shares, which can be expressed as s = [s1, s2, ..., s N ] T , where S n Indicates the number of n-type shares in maintenance. Allocation is limited by the resource pool size:

[0070]

[0071] In the following, the set of all allocations S that satisfy the above relationship is called the allowed region S, which is obviously a finite set.

[0072] Shares are created by the system and released when an unplanned amount coverage increase is requested and / or when the system detects a loss. When a coverage requires new shares to support the requested resource transfer, it sends a request to the system, which will be accepted or rejected by the system. Upon acceptance, the requested share will be created and maintained until it is released. Technically, both the request to create and the request to release can come, for example, randomly. As an implementation variant, it can be assumed that: (i) for each share type n, a share creation request is sent as a request with λ n Poisson events arrive at a rate of μ; and / ii) the lifetime of each n-type fraction is independent of μ n The mean exponential random variable. Therefore, a state model structure can be applied to capture the processing of share admission control, such as Fig.18 as shown in . Fig.18 An exemplary asynchronous share admission is shown, where the status is updated when a request for an unplanned amount coverage increase arrives and / or when a loss is detected by the system. Fig.18 The reference numbers in the table are: (1) rejecting a request to create a share, (2) accepting a request to create a share of type i, and (3) accepting a request to release a share of type j. The gray area above is the accessible area. Each state represents the resource allocation. This makes it so that the system cannot make any decision that would lead to an infeasible resource allocation that violates the relationship given above. Note that requests for share release are always accepted.

[0073] Typically, the incoming request q and the system's binary decision d are defined as as well as After conversion, the state S post are q, d and the state before the transition S pre Function:

[0074]

[0075] As discussed above, when receiving an unplanned amount coverage increase request q, the system makes a binary decision d∈{1,0}. If d is only q and the pre-transition state S pre function, then we can say that the system has a consistent partitioning strategy d = D(q, spre). Since the system cannot overload the limited resource pool or refuse to release shares, given the admissible area Partition strategy D is only valid if

[0076]

[0077] For any valid partitioning strategy D, the following are true:

[0078]

[0079] Now we move from an implementation variant of an asynchronous state modeling structure with shared access to an implementation variant with a synchronous state modeling structure, in which the update of the state, i.e. the response of the system, is triggered by a request arriving at an arbitrary or random time. Technically, the system handles requests in a framework approach, in which all incoming requests, regardless of type, are buffered, for example, in a queue, and are responded to sequentially at the end of each cycle of operation. This leads to a synchronous state model, in which the state update is triggered periodically. The synchronous state model obviously has the same admissible area as the corresponding asynchronous state modeling structure. But there are more complex conditions for transitions between states. By normalizing the operation cycle length and / or weighting it to 1, the system makes sequential decisions on buffered requests with discrete time indices. The vector q(t) = [q1(t), q2(t), ..., q Qt (t)] can be used, for example, to represent the request queue buffered during the tth operation cycle, where Q t is the total number of partition requests that arrive in this cycle. Given any strategy d = D(q, s), the state at (t+1) is

[0080]

[0081] As mentioned above, the following relationship is implemented

[0082]

[0083] It is important to note that, using the above relationship for s(t+1) and using the memoryless distribution of request arrivals and a consistent partitioning strategy, the synchronization state model for share admission is Markov.

[0084] Figures 2 to 4 A block diagram schematically shows an exemplary overview of a share, wherein Figure 2 shows the color legend, and Figure 3 The corresponding share processing is shown.

[0085] The present invention also technically allows modeling cash flows and linking them to states or state transitions according to corresponding rates - this allows the present value of cash flows to be generated and / or calculated at each discrete point in time. Figure 5 Visualization. Figure 5 A block diagram schematically illustrating an exemplary state transition cash flow modeling structure and / or a three-state transition cash flow modeling structure is shown. Figure 6 to Figure 7 A block diagram schematically illustrating an exemplary overview of how a Markov chain modeling structure is used is shown, wherein Figure 6 shows a simple Markov chain modeling example, and Figure 7 A simple Markov chain calculation of the premium cash flow linked to state A is shown. Figure 7 An example of 1000 customers is shown: in the first year, 100*1000=100,000 are received. If 20% of the customers die in the second year, then the active customers are 800, and 90*800=72,000 are received (instead of 90*1000=90,000).

[0086] The mathematical representation of the present value formula for the (sub)set of modeled cash flows at the share level is as follows: ...calculates the present value of the set CF of cash flow amount vectors of state S at time period t …calculates the cash flow amount of state S at time period t …the discount rate at time t during the period from calculation period t to calculation period t+k ...the conversion rate from state S to state T at calculation period t …calculate the cash flow amount from state S to state T at time period t

[0087]

[0088] It is noted that the implementation of the calculation engine can be performed at the policy level using deterministic conversion rates and interest rates processed discretely, for example. However, the present invention can also be implemented using parts of the calculation engine where stochastic support is simulated at the portfolio level.

[0089] Reference numerals list

[0090] 1 Electronic Digital Finite State System

[0091] 10Data Storage

[0092] 101 Data Structures

[0093] 1011 modular digital conversion functions

[0094] 1012 Markov chain data elements

[0095] 11Markov chain modeling structure / predictor / simulator

[0096] 111 applied conversion function

[0097] 112 The interest parameter value applied

[0098] 113 The conversion rate parameter value of the application

[0099] 114 Markov chain states

[0100] 115 Output parameter values ​​of flexible Markov chain structure

[0101] 12Simulation engines for automated processing / stochastic Markov data processing

[0102] 121 Probability parameter value propagation processing

[0103] 122 Automatic pricing processing

[0104] 123 Individual mortality-related measured parameters based on measured parameters associated with individuals 2 / 2i / 2ii

[0105] 124 Data Structure Container / Portfolio

[0106] 1241 Data structure capturing loss coverage / risk transfer parameters for risk transfer (policy / risk transfer)

[0107] 12411 Risk Transfer Parameters

[0108] 125 Digital Library

[0109] 13Finite State Machine Engine

[0110] 131 Object status trigger processing

[0111] 1311 Sequence of processing steps

[0112] 132 Digital objects

[0113] 1320 Persistent Storage / Digital Library

[0114] 1321 Defined Parameter Set

[0115] 13211 Structural parameters

[0116] 13212 Status Parameters

[0117] 13213 Digital Policy

[0118] 132131 Digital Policy Parameters

[0119] 132132 Policy life cycle status

[0120] 1322 Object Status

[0121] 13221The initial state of the object

[0122] 13222Intermediate state of the object

[0123] 13223The final state of the object

[0124] 1323 Finite State Automata

[0125] 13231 Possible states of the object

[0126] 13232 Transition rules / functions associated with possible states 132321 Processing steps required for transition 132322 Conditional parameter values ​​required for transition

[0127] 132323 Automatic conversion trigger

[0128] 132324 conversion threshold

[0129] 132325 conversion process

[0130] 1323251 Connection Processing

[0131] 1323252 Forging treatment

[0132] 1323253Jointing Process

[0133] 13233 Initial Digital Object Configuration

[0134] 133 Initial object configuration processing

[0135] 14 output signal generator

[0136] 140 electronic output signal

[0137] 141 Trigger signal for automatic underwriting processing

[0138] 1411 Automatic Electronic Underwriting System

[0139] 142 Control and threshold signals for automated portfolio management and administration of applicable

[0140] 1421 Automated digital portfolio management system electronically controlled by signals from a Markov chain predictor 11

[0141] 143 Based on the measured parameter values ​​of the wearable device / IoT sensor 15, the triggered automatic digital twin application controlled by the output signal generated by the digital individual measurement engine 3

[0142] 144 digital offers available

[0143] 145 New digital risk transfer policy automatically generated

[0144] 15 Wearable devices / IoT sensors (input devices and sensors)

[0145] 151 somatosensory device

[0146] 152 Environmental Sensing Device

[0147] 16Data transmission network

[0148] 17Data transmission interface

[0149] 2 Real-world risk exposure individuals

[0150] 21 Physical Body Structure

[0151] 22 Invisible physical condition (psychological)

[0152] 23 Subsystems of Real-World Individuals

[0153] 231,232,233,…,23iSubsystem 1,…,i

[0154] 24 Individual Environment / Ecosystem

[0155] 241,242,243,…,24iSubsystem 1,…,i

[0156] 25 Physical real-world events that physically affect individuals by causing measurable physical damage / bodily injury or loss to them

[0157] 251 Diseases

[0158] 252 Accident

[0159] 253 Natural disasters (earthquakes / storms / hurricanes / floods, etc.)

[0160] 3Digital individual measurement engine

[0161] 31Digital Intelligence Layer

[0162] 311 Machine Learning

[0163] 312 Neural Network

[0164] 32 Physical parameters of real-world individuals

[0165] 33 State parameters of real-world individuals

[0166] 331 Physiological (physical) state parameters

[0167] 332 Psychological state parameters

[0168] 333 Habit / behavioral status parameters (nutrition / exercise, etc.)

[0169] 333 Environmental status parameters (living conditions, etc.)

[0170] 34 Data structures representing the state of each of the multiple subsystems of a real-world individual 35 Digital Danger and / or Life Risk Event Robot

[0171] 351 Simulation

[0172] 352 Synchronization

[0173] 353 Dual Link: Sensory / Measurement / Data Collection-Wearable Devices / IoT Sensors 15

[0174] 36 Digital Ecosystem Copy Layer

[0175] 361,362,363,…,36i Virtual subsystems of virtual representations of ecosystems

[0176] 37 individual digital objects / element layers

[0177] 371 Simulation

[0178] 372 Synchronization

[0179] 373 Links: Sensory / Measurement / Data Acquisition - Wearables / IoT Sensors 15

[0180] 38 digital individual copy layers

[0181] 381,382,383,…,38i Virtual subsystems of real-world individuals< / bigdecimal>

Claims

1. An electronic digital finite state system (1) for automatically processing a digital object (132) by object state triggered processing (131), the digital object (132) being tractable or defined by a parameter set (1321) having definable parameter values, the parameter set (1321) comprising at least a structural parameter (13211) or a state parameter (13212) associated with the digital object (132), wherein: A digital set (1321) of defined parameter values ​​defines an initial state and / or setting (1322) of the object (132), wherein the object (132) is automatically processed by the digital system (1) by processing the digital object (132) by a series of processing steps (1311), the digital object (132) transitioning from the initial state (13221) via an intermediate state (13222) to a final state (13223) by the series of processing steps (1311), characterized in that The digital system (1) comprises a finite state machine engine (13), wherein processing of each digital object (131) is based on a finite state automaton (1323) included in or associated with the digital object (131), the finite state automaton (1323) comprising all possible states (13231) of the digital object (132) and associated transition rules (13232), the transition rules (13232) comprising at least processing steps (132321) and conditional parameter values ​​(132322) for processing the digital object (132) from one finite state (1322) to a next finite state (1322) by the finite state machine engine (13), and As part of an initial object configuration process (133), the possible states (13231) and the transformation rules (13232) of the digital object (132) can be configured within the digital object (132) and processed implicitly, wherein the initial state and / or setting (13221) of the object (132) includes at least the possible states (13231) and the associated transformation rules (13232) as an initial digital object configuration (13233).

2. The electronic digital finite state system (1) according to claim 1, characterized in that In the event of a defined physical impact event (25), the digital set (1321) of defined parameter values ​​is given by a digital policy parameter value (132131) for a policy (13213) for loss coverage associated with the exposure of the risk exposure unit (2), wherein the processing status (1322) is given by the policy lifecycle status (132132).

3. The electronic digital finite state system (1) according to claim 1, characterized in that The conversion rule (111) includes at least an automatic conversion trigger (132323) and / or a conversion threshold (132324) and a conversion process (132325), and the conversion process (132325) includes at least a connection process (1323251) and / or a forging process (1323252) and / or a joining process (1323253).

4. The electronic digital finite state system (1) according to claim 3, characterized in that The automatic conversion trigger (132323) triggers conversion automatically or by default, wherein the automatic conversion is triggered when the digital system (1) detects that at least one trigger value or threshold value (132324) is about to be reached or exceeded.

5. The electronic digital finite state system (1) according to claim 4, characterized in that The at least one trigger value or threshold (132324) is monitored by the finite state machine engine (13), wherein the trigger (132323) triggers the state transition if a parameter value associated with the digital object (132) is reached or exceeded.

6. The electronic digital finite state system (1) according to one of claims 2 to 5, characterized in that The digital system comprises a simulation engine (11) comprising a data structure for capturing and / or storing discretely processed deterministic transition rate parameter values ​​and interest rate parameter values ​​for data processing according to the policy, wherein portions of the processing are used with discretely processed stochastic transition rates and interest rates to perform simulations at a portfolio level, The digital system comprises an interface to the finite state machine engine, wherein, for digital object processing, at least the interest parameter value and / or the conversion rate parameter value is user-specific and flexibly configurable and / or selectable from an associated digital library, and wherein, within the finite state machine engine, setting parameters and random conversion rates are processed and modeled or simulated by the finite state machine engine, and The system includes a share generator wherein data processing is partitioned by occurring at the share level and accumulated by benefit status and quote level, wherein a share is created each time a policy benefit is impacted by an unplanned increase in the amount of insurance, and wherein, for each newly applied risk transfer, each benefit starts with a share, each share being associated with a specific risk transfer version, coverage, and rate at the corresponding point in time when the share was generated.

7. The electronic digital finite state system (1) according to claim 6, characterized in that Each finite state of an insurance policy (13213) consists of one or more insurance benefits shares.

8. The electronic digital finite state system (1) according to claim 6, characterized in that The calculation engine processes the parameter values ​​on a per-share basis and / or cyclically, thereby providing traceable pricing parameter value generation and output.

9. The electronic digital finite state system (1) according to one of claims 6 to 8, characterized in that The calculation engine includes an object or product framework that sets the entire set of allowable configuration options at the product and / or premium and / or rate level, a subset of which can be used to configure individual risk transfers.

10. The electronic digital finite state system (1) according to claim 9, characterized in that The product framework also includes a product entity framework, wherein the initial settings of the product framework include at least annual and monthly premium frequencies.

11. The electronic digital finite state system (1) according to claim 9, characterized in that The initial setup of the product framework includes at least annual and monthly premium frequencies, wherein the parameter values ​​can be increased over time.

12. The electronic digital finite state system (1) according to one of claims 9 to 11, characterized in that The product framework also includes a premium entity framework that defines the full set of allowable configuration options at the premium level, a subset of which can be used by a premium.

13. The electronic digital finite state system (1) according to one of claims 9 to 12, characterized in that The product framework also includes a rate entity framework that defines the full set of allowable configuration options at the rate level, a subset of which can be used by a rate.

14. The electronic digital finite state system (1) according to claim 13, characterized in that Applicable premium rate schedules are located at the rate level to provide the ability to always apply the latest premium rate schedule based on the policy share.

15. The electronic digital finite state system (1) according to one of claims 6 to 14, characterized in that Data processing or computation requests enhance configuration flexibility by breaking down the computation requests into micro-computation requests at the quote, premium, and share levels, which are pre-processed and run in a dynamic sequence.

16. The electronic digital finite state system (1) according to claim 15, characterized in that The computation engine includes (2): (i) pre-processing by automatically creating micro-computation requests at the quote, premium and / or share level and filling them, and (ii) core processing by executing the micro-computation requests, wherein the core computation always remains the same.

17. The electronic digital finite state system (1) according to one of claims 6 to 16, characterized in that For the data processing performed by the finite state machine engine (13), one or more transition functions (13232) are configurable via the data interface (17) and / or are selectable from the digital library (1320), the transition functions linking at least two states within the finite state machine engine, wherein all finite states are linked to previous and subsequent states that provide data processing for the entire configurable digital object.

18. The electronic digital finite state system (1) according to one of claims 6 to 17, characterized in that The dependencies between the rate parameters can be adapted within the finite state machine engine.

19. The electronic digital finite state system (1) according to one of claims 6 to 18, characterized in that The finite state machine engine (13) also includes elements that provide flexible configuration for use of a combined model structure of stochastic interest parameter values ​​and / or mortality parameter values.

20. The electronic digital finite state system (1) according to one of claims 6 to 19, characterized in that The data processing of the interest parameter and the mortality parameter by the finite state machine engine (13) is configured by an affine data processing structure, wherein the finite state machine engine becomes a model structure that can be tracked during the propagation of the parameter values ​​to a defined future time window.

21. The electronic digital finite state system (1) according to one of claims 6 to 20, characterized in that For the flexible configuration, the digital system (1) comprises adaptable calculation configuration files and / or trees which can be processed by the calculation engine of the digital system.

22. The electronic digital finite state system (1) according to one of claims 6 to 21, characterized in that The finite state machine engine (13) is implemented as a continuous time finite state machine engine (13) with a finite or countably infinite state space providing a random process for parameter propagation.

23. The electronic digital finite state system (1) according to one of claims 6 to 22, characterized in that The finite state machine engine (13) is provided only to time-uniform finite state processing (131 / 1311), wherein all probability values ​​providing a measure of life risk within a future time window are generated based on finite state properties.

24. The electronic digital finite state system (1) according to one of claims 6 to 23, characterized in that The digital library (1320) is accessible to multiple users, wherein the conversion function and / or the interest parameter value and / or the conversion rate parameter value of a user is accessible to another user via the data interface and is applicable to the finite state machine engine (13) of the other user.

25. The electronic digital finite state system (1) according to claim 24, characterized in that The conversion function and / or the interest parameter value and / or the conversion rate parameter value of the first user are only accessible upon request by the second user and / or upon approval or activation by the first user to the second user.

26. The electronic digital finite state system (1) according to one of claims 1 to 25, characterized in that The digital system (1) also includes a Markov chain modeling structure and / or predictor for mortality probability parameter value propagation (121) and for dynamically and automatically influencing coverage pricing (122) by processing a plurality of individual mortality related measurement parameters (123) associated with a portfolio (124) of loss coverages (1241) of risk exposed individuals (2), wherein each loss coverage (1241) held in association with the portfolio (124) is set by a risk transfer parameter (12411) of the loss coverage (1241) to define a risk transfer policy for the individual risk transfer (1241) based on the parameter, wherein a Markov chain modeling structure (11) is applied in combination with configurable elements comprising at least states (114) and / or state transitions (113) and / or cash flows (112) specific to said risk transfer (1241), and wherein stochastic Markov data processing is applied to said Markov chain structure (11) through a series of possible events (25 / 25i), wherein said probability value of each event (25 / 25i) depends only on the states (114) obtained in the preceding events (25), wherein the digital system (1) comprises a simulation engine (12), the simulation engine (12) comprising a data structure (11) for capturing and / or storing deterministic transition rate parameter values ​​(113) and interest rate parameter values ​​(112) of a state discrete process (121) for data processing according to an insurance policy (1241), wherein parts of the process (12) are used together with the discretely processed random transition rates (113) and interest rates (112) to perform simulations at a portfolio level, wherein the digital system (1) comprises an interface (13) to the Markov chain structure (11), wherein, for the stochastic Markov data processing (2), interest parameter values ​​(112) and conversion rate parameter values ​​(113) are user-specific and flexibly configurable and / or user-specifically selectable from an associated digital library (125), and wherein, within the stochastic Markov data processing arrangement (111, ..., 114) and the stochastic conversion rates (113), these underlying rates (113) are processed and modeled by the finite state Markov chain structure (11), and Wherein, for the data processing performed by the finite state Markov chain structure (11), one or more transition functions (111) are configurable via the data interface (13) and / or selectable from the digital library (125), the transition function (111) linking at least two states (114) within the Markov chain structure (11), wherein all states (114) of the Markov chain structure (11) are linked to previous states and subsequent states (114) providing data processing for the entire configurable Markov chain structure (11).

27. The electronic digital finite state system (1) according to claim 26, characterized in that The digital system and platform (1) include a signal generator (14), which automatically generates an electronic signal (140, ..., 145) based on the output parameter value of the electronic Markov chain structure (11), and the electronic signal is transmitted to the automatic underwriting system of the digital system (1), and the automatic underwriting system triggers at least one automatic underwriting process (141) by automatically assigning at least one risk-exposed individual (2) processed by the Markov chain structure (11) to a risk transfer (1241) associated with a future occurrence of a physical event (25) that physically affects the at least one risk-exposed individual (2).

28. The electronic digital finite state system (1) according to one of claims 26 or 27, characterized in that The digital system and platform (1) includes a signal generator (14) which automatically generates an electronic signal (140) based on the output parameter value (115) of the electronic Markov chain structure (11), the electronic signal (140) being transmitted to an automatic digital portfolio management system (1421) of the digital system (1), the automatic digital portfolio management system (1421) automatically adjusting a threshold of at least one risk-exposed individual (2) processed by the Markov chain structure (11), the at least one risk-exposed individual (2) being automatically assigned to the portfolio (124), wherein the system (1) automatically covers the occurrence of losses and / or damages and / or injuries to the individual (2) associated with a future occurrence of a physical event (25) that physically affects the at least one risk-exposed individual (2).

29. The electronic digital finite state system (1) according to one of claims 26 to 28, characterized in that The correlation between the flexibly configurable conversion rate parameter and the interest rate parameter is applicable within the finite state Markov chain structure (11).

30. The electronic digital finite state system (1) according to one of claims 26 to 29, characterized in that The finite state Markov chain structure (11) also includes elements that provide flexible configuration for the use of a combined model structure of random interest parameter values ​​and / or mortality parameter values.

31. The electronic digital finite state system (1) according to one of claims 26 to 30, characterized in that The stochastic Markov data processing of the interest parameter and the mortality parameter is configured by an affine data processing structure, wherein the finite state Markov chain structure (11) becomes a model structure that can be tracked during the propagation of the parameter values ​​into a defined future time window.

32. The electronic digital finite state system (1) according to one of claims 26 to 31, characterized in that For the flexible configuration, the digital system (1) comprises adaptable calculation configuration files and / or trees that can be processed by the simulation engine (12) of the digital system (1).

33. The electronic digital finite state system (1) according to one of claims 26 to 32, characterized in that The Markov chain structure (11) is implemented as a continuous-time Markov chain structure (11) with a finite or countably infinite state space providing a random process for parameter propagation.

34. The electronic digital finite state system (1) according to one of claims 26 to 33, characterized in that The Markov chain structure (11) is only provided for time-uniform Markov chain processing, where all probability values ​​providing a measure of life risk within a future time window are generated based on the Markov property.

35. A digital method of an electronic digital finite state system (1) for automatically processing a digital object (132) by object state triggered processing (131), the digital object (132) being tractable or defined by a parameter set (1321) with definable parameter values, the parameter set (1321) comprising at least structural parameters (13211) or state parameters (13212) associated with the digital object (132), wherein: A digital set (1321) of defined parameter values ​​defines an initial state and / or setting (1322) of the object (132), wherein the object (132) is automatically processed by the digital system (1) by processing the digital object (132) by a series of processing steps (1311), the digital object (132) transitioning from the initial state (13221) via an intermediate state (13222) to a final state (13223) by the series of processing steps (1311), characterized in that The processing of each digital object (131) by the finite state machine engine (13) of the digital system (1) is based on a finite state automaton (1323) included in or associated with the digital object (131), the finite state automaton (1323) including all possible states (13231) of the digital object (132) and associated transition rules (13232), the transition rules (13232) including at least processing steps (132321) and conditional parameter values ​​(132322) for processing the digital object (132) from one finite state (1322) to a next finite state (1322) by the finite state machine engine (13), and As part of an initial object configuration process (133), the possible states (13231) and their transformation rules (13232) of the digital object (132) are configured within the digital object (132) and the possible states (13231) and their transformation rules (13232) are implicitly processed, wherein the initial state and / or setting (13221) of the object (132) includes at least the possible states (13231) and the associated transformation rules (13232) as an initial digital object configuration (13233).

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